Discover and read the best of Twitter Threads about #earthengine

Most recents (11)

With the release of #DynamicWorld and other #global maps from the last few years, I have some (rambling) thoughts on global products, the challenge of accuracy assessment, and why I think there is still a lot of room for high-quality local/regional predictive mapping (1)
First, these global products are super cool! I played around with Google's DynamicWorld 10 m land cover today and being able to see the probabilities for each class for each pixel is great and being able to do this for every #Sentinel image through time is really unique (2)
However, at #local scales, they can be hit-or-miss, and none are ideal for every need. Some fail to pass a visual inspection (the "eye test") and even the best products require (in my opinion) local quantitative accuracy assessment before use for decision making or in models. (3)
Read 32 tweets
A thread about the newly launched #DynamicWorld landcover dataset by #Google. I had early access and explored this dataset in detail. You may be very excited about this dataset, but likely for the wrong reasons. Sharing some insights, potential use cases, and pitfalls. 1/n
First of all - what is it? It's a Landcover dataset based on Sentinel-2 data - but with a key difference. Rather than a static snapshot, it is a time series. *Every* Sentinel-2 scene is classified with class probabilities for 9 landcover classes. 2/n
It is an incredible technological feat. The dataset contains not just every Sentinel-2 scene from the archive, but every new scene is classified and made available in just a few minutes to all #EarthEngine users a through a dynamic collection 3/n developers.google.com/earth-engine/d…
Read 12 tweets
Timelapse of Hunga Tonga #volcano eruption on Jan 15 2022. Created using the #streamlit web app 👇

App: streamlit.gishub.org
Location: 20.536°S 175.382°W
Time: 03:00-07:00 UTC
Satellite: GOES-17 CMI Full Disk

#EarthEngine #geemap #eochat #gischat #dataviz
Night view
A closer look
Read 6 tweets
Happy New Year🎆 You can now create sea surface temperature timelapse with a colorbar using the #streamlit web app in <60 seconds 👇

App: streamlit.gishub.org
GitHub: github.com/giswqs/streaml…

#EarthEngine #geemap #eochat #gischat #oceancolor #dataviz #geospatial
Here is a demo using the @streamlit web app for creating ocean color timelapse with #MODIS Aqua data @chris_harrod

Keep in mind that you can create timelapse by selecting a yearly, quarterly, weekly, or daily frequency
Read 3 tweets
An interactive web app for creating timelapse from #GOES #weather satellites. Built with #streamlit #geemap #EarthEngine. Go to streamlit.gishub.org and select "Create Timelapse" from the sidebar menu.

GitHub: github.com/giswqs/streaml…

#dataviz #eochat #geospatial #wildfire
Bomb Cyclone on 2021-10-24
Creek Fire, CA on 2020-09-05
Read 5 tweets
An interactive web app for creating timelapse of annual #Landsat imagery (1984-2021) for any location around the global. Built with #streamlit #geemap #EarthEngine. Go to streamlit.gishub.org and select "Create Timelapse" from the sidebar menu

GitHub: github.com/giswqs/streaml…
Read 12 tweets
Where are India’s biologically-significant Open Natural Ecosystems (ONEs)?

Thread 👇🏽 on a new, open, and analysis-ready dataset on the distribution of India’s beautiful and beleaguered semi-arid Open Natural Ecosystems. (Representative image for each of the ecosystem types).
A large fraction of India’s landmass is semi-arid (annual rainfall < 1000 mm). The native vegetation in this zone is made up of grass, herbs and shrubs. They are often naturally without trees, and if at all trees do occur, cover is sparse. Yet, ONEs are staggeringly diverse.
Mirroring the diversity of habitats, ONEs also have a remarkable diversity of animal species, many of which are unique to the Indian subcontinent.
Read 19 tweets
Massive new #QGIS plugin: #GEE Timeseries Explorer offers instant access to #EarthEngine image collections! Fetch #Landsat, #Sentinel2, or #MODIS #timeseries for any location and visualize images: geetimeseriesexplorer.readthedocs.io 🧵
Choose from our predefined collections, for instance merged & #cloud masked #Landsat TM, ETM+, OLI surface reflectance, cloud masked Sentinel2AB L2A, or just define your awesome custom collection in the built-in #Python editor.
Extract time series profiles for #training / #validation by clicking on map, or by navigating through vector point file. Download raw time series data for #sample-based workflows efficiently with parallel download.
Read 6 tweets
#geemap new feature: changing layer visualization interactively with a GUI. This is a very challenging implementation with many hours of work and >1000 lines of code.

App: gishub.org/geemap-vis

#EarthEngine #GIS #remotesensing #python #jupyter #mapping #ipyleaflet #dataviz
Just added a new option for turning all layers on/off.
Read 3 tweets
Before making New Years resolutions, I thought I’d start by looking back with gratitude at 🔟 things I’ve accomplished this past year.

I want to say a big 🙌🏽THANK YOU🙌🏽 to my colleagues, friends, and family for your support on this journey! #ThankYou2019
Finished my work at @yalefes and moved to a new country.🛫🇺🇸🛬🇩🇪

Though I still miss my colleagues and the students I worked with, I'm still collaborating with them so it isn't really goodbye!
💡🗺️ Started a geospatial consulting business. 🗺️💡

Glad to also be working with fellow @geospatialwomen entrepreneur @JuliaWagemann to kick off new ideas in 2020!

I want to also thank Emmanuel Mondon of @Maxar for connecting me to the #EU #EO space!
Read 13 tweets
As India waits anxiously for the #monsoon to break over a parched subcontinent, here’s a look at the advance and retreat of the 2018 monsoon. #EarthEngine@googleearth⁩ ⁦@EarthOutreach
As the #monsoon clouds drape and dance over the subcontinent (see tweet above), beneath those clouds, an entire parched landmass bursts into life as the rains fall. See for yourself why the monsoon is truly the beating heart of India. #EarthEngine @googleearth @EarthOutreach
@googleearth @EarthOutreach In the pre-#Monsoon half of the year, south India seems to have suffered a greater rainfall deficit this year (i.e., more orange than blue) than the last. Consecutive deficit years for Gujarat and Maharashtra, whereas north India seems marginally better off so far this year.
Read 4 tweets

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